1. Handbook
  2. Subjects
  3. Categorical Data: Models and Methods

Categorical Data: Models and Methods (MAST90099)

Graduate courseworkPoints: 12.5On Campus (Parkville)

You’re viewing the 2018 Handbook:
Or view archived Handbooks
You’re currently viewing the 2018 version of this subject

Overview

Availability
Semester 2
FeesLook up fees

Introduction to and revision of conventional methods for contingency tables especially in epidemiology:

  • Odds ratios and relative risks;
  • Chi-squared tests for independence;
  • Mantel Haenszel methods for stratified tables;
  • Methods for paired data.


The exponential family of distributions;

  • Generalized Linear Models (GLMs);
  • Parameter estimation for GLMs;
  • Inference for GLMs, including the use of score, Wald and deviance statistics (including residuals) for confidence intervals and hypothesis tests.


Binary variables and logistic regression models:

  • Methods for assessing model adequacy;
  • Nominal and ordinal logistic regression for categorical response variables with more than two categories;


Count data and Poisson regression models:

  • Log-linear models.


Software:

  • Fitting GLMs in Stata and R.

Intended learning outcomes


  • Understand the mathematical theory behind generalised linear models (GLMs) to analyse categorical data with proper attention to the underlying assumptions.
  • Appreciate that most conventional methods of analysis of contingency table data are special cases of GLMs.
  • Operate the Stata and R statistical packages to fit GLMs to data, extract, summarise, present and report the results.
  • Emphasise the importance of the practical interpretation and communication of results to colleagues and clients who are not statisticians.

Generic skills

  • Independent problem solving,
  • Facility with abstract reasoning,
  • Clarity of written expression,
  • Sound communication of technical concepts

Last updated: 3 November 2022